TL;DR
The results show that the proposed approach enables accurate, robust, and explainable disease detection, making it a promising tool for precision agriculture and early diagnosis in mango orchards.
The combination of hybrid feature fusion, NCA-based feature optimization, and Bayesian-optimized ensemble classification leads to enhanced discriminative power, greater robustness, and better generalization performance for the system in citrus disease identification in a real-world agricultural setting, as demonstrated...
The findings revealed that the proposed MaxViT Swin–IGWO hybrid framework detects mango leaf diseases with superior performance, outperforming both conventional and contemporary alternatives.
The proposed Sugarcane Leaf Disease Detection and Classification System provides a fast, accurate, and user-friendly solution for automated disease diagnosis and contributes to improved crop management, reduced crop losses, and enhanced agricultural productivity.
Background: Legumes, such as beans, are important in worldwide agriculture because of their nutritional value and soil-enriching qualities. However, bean crops are susceptible to diseases such as angular leaf spot and rust, which may reduce production and quality. Disease identification that is both effective and timel...
India is one of the biggest producers and exporters of mangoes in the world, yet its cultivation is persistently threatened diseases that reduce yield, fruit quality, and orchard longevity. Traditional disease diagnosis is based on agronomists' hand visual inspection, which is a laborious, subjective, and challenging t...
The results demonstrate that the combination of DenseNet201 and Soft Voting provides an accurate and effective approach for rice leaf disease classification and has strong potential as an early disease detection tool in agriculture.
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